Optimal iterative increasing binary morphological filters

Edward R. Dougherty; Yeqing Zhang; Yidong Chen

doi:10.1117/1.601086

1 December 1996 Optimal iterative increasing binary morphological filters

Edward R. Dougherty, Yeqing Zhang, Yidong Chen

Optical Engineering, Vol. 35, Issue 12, (December 1996). https://doi.org/10.1117/1.601086

Rather than design an optimal filter over a large window, which may be computationally impossible or require unacceptable computation time, one can design an iterative filter, each stage of which is designed over a small window with acceptable design time. If a twostage iterative filter is designed with each stage optimally designed over a window, then the iterative filter is a suboptimal approximation to the optimal filter over the larger window formed as the dilation of the small window with itself. Using image-noise models, three basic questions regarding optimal increasing iterative filters are statistically addressed: How many iterations are required before there is only a negligible increase in filter performance? As the number of iterations increases, how good is filter performance in comparison with large-window noniterative filters? What are the logical and probabilistic relations between noniterative and approximating interative filters? Iterative-filter performance is seen to be only slightly suboptimal, in return for tractable design. A key conclusion is that, while in terms of logic there may be a significant difference between a noniterative and approximating iterative filter, their probabilistic difference as operators on random sets can be neglible. This fundamental point is discussed in the context of application to digital document processes, which form a key area of application.

Citation Download Citation

Edward R. Dougherty, Yeqing Zhang, and Yidong Chen "Optimal iterative increasing binary morphological filters," Optical Engineering 35(12), (1 December 1996). https://doi.org/10.1117/1.601086

Published: 1 December 1996

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available